On the trend of developing smart manufacturing and digitalization factory, the automation of both production line and quality inspection has become an essential element nowadays. In comparison with conventional fan manufacturing factories which make use of diagnostic inspections by human senses, in this research an approach named multi-sensor data fusion to achieve more accurate and reliable results by using two kinds of sensors such as accelerometer and microphone is utilized.

  In considering practical application requirement, signals sampling for 3 seconds is set, and RMS and FFT analytical methods are employed in this study. Feature extraction is then followed to solve the problem of high data dimensions due to multi-sensor data fusion. Two kinds of machine learning models, SVM and decision tree, are applied using the labeled samples that had been classified by the professional fan quality controllers. Using 36 samples for training as well as other 9 samples for testing, and the support vector machine and decision tree model are found accurate in making correct diagnosis, which validates the model of the diagnosis system proposed in this thesis.

Key words: Fan faults diagnosis, Multi-sensor data fusion, Machine learning, SVM, Decision tree